Shank3 Modulates Sleep and Expression of Circadian Transcription Factors differential expression
October 29, 2018
Description
This is the report of the analysis made for the paper Shank3 Modulates Sleep and Expression of Circadian Transcription Factors by Ashley M. Ingiosi, Taylor Wintler, Hannah Schoch, Kristan G. Singletary, Dario Righelli, Leandro G. Roser, Davide Risso, Marcos G. Frank and Lucia Peixoto.
Autism Spectrum Disorder (ASD) is the most prevalent neurodevelopmental disorder in the US that often co-presents with sleep problems. Sleep impairments in ASD predict the severity of ASD core diagnostic symptoms and have a considerable impact on the quality of life of caregivers. However, little is known about the underlying molecular mechanism(s) of sleep impairments in ASD. In this study we investigated the role of Shank3, a high confidence ASD gene candidate, in the regulation of sleep. We show that Shank3 mutant mice have problems falling asleep despite accumulating sleep pressure. Using RNA-seq we show that sleep deprivation doubles the differences in gene expression between mutants and wild types and downregulates circadian transcription factors Per3, Dec2, and Rev-erb\(\alpha\). Shank3 mutants also have trouble regulating locomotor activity in the absence of light. Overall, our study shows that Shank3 is an important modulator of sleep and circadian activity.
Differential Expression Analysis
Importing data
Importing data and filtering out those genes with cpm lesser than 1. We use the filtered.data method in NOISeq package.
countMatrix <- ReadDataFrameFromTsv(file.name.path="./data/refSEQ_countMatrix.txt")## ./data/refSEQ_countMatrix.txt read from disk!# head(countMatrix)
designMatrix <- ReadDataFrameFromTsv(file.name.path="./design/all_samples_short_names.tsv")## ./design/all_samples_short_names.tsv read from disk!# head(designMatrix)
filteredCountsProp <- filterLowCounts(counts.dataframe=countMatrix,
is.normalized=FALSE,
design.dataframe=designMatrix,
cond.col.name="gcondition",
method.type="Proportion")## features dimensions before normalization: 27179## Filtering out low count features...
## 14454 features are to be kept for differential expression analysis with filtering method 3Plot PCA of log unnormalized data
PCA Plot of filtered not-normalized data.
PlotPCAPlotlyFunction(counts.data.frame=log1p(filteredCountsProp),
design.matrix=designMatrix,
shapeColname="condition", colorColname="genotype", xPCA="PC1", yPCA="PC2",
plotly.flag=TRUE, show.plot.flag=TRUE, prefix.plot="Prop-Un-Norm")## [1] TRUEControl Genes
Negative control genes
Loading Negative Control Genes to normalize data
library(readxl)
sd.ctrls <- read_excel(path="./data/controls/Additional File 4 full list of BMC genomics SD&RS2.xlsx", sheet=1)
sd.ctrls <- sd.ctrls[order(sd.ctrls$adj.P.Val),]
sd.neg.ctrls <- sd.ctrls[sd.ctrls$adj.P.Val > 0.9, ]
sd.neg.ctrls <- sd.neg.ctrls$`MGI Symbol`
sd.neg.ctrls <- sd.neg.ctrls[-which(is.na(sd.neg.ctrls))]
int.neg.ctrls <- sd.neg.ctrls
int.neg.ctrls <- unique(int.neg.ctrls)
neg.map <- convertGenesViaMouseDb(gene.list=int.neg.ctrls, fromType="SYMBOL",
"ENTREZID")
# sum(is.na(neg.map$ENTREZID))
neg.ctrls.entrez <- as.character(neg.map$ENTREZID)
ind.ctrls <- which(rownames(filteredCountsProp) %in% neg.ctrls.entrez)
counts.neg.ctrls <- filteredCountsProp[ind.ctrls,]positive control genes
Loading Positive Control Genes to detect them during the differential expression step.
## sleep deprivation
sd.lit.pos.ctrls <- read_excel("./data/controls/SD_RS_PosControls_final.xlsx",
sheet=1)
colnames(sd.lit.pos.ctrls) <- sd.lit.pos.ctrls[1,]
sd.lit.pos.ctrls <- sd.lit.pos.ctrls[-1,]
sd.est.pos.ctrls <- read_excel("./data/controls/SD_RS_PosControls_final.xlsx",
sheet=3)
sd.pos.ctrls <- cbind(sd.est.pos.ctrls$`MGI Symbol`, "est")
sd.pos.ctrls <- rbind(sd.pos.ctrls, cbind(sd.lit.pos.ctrls$Gene, "lit"))
sd.pos.ctrls <- sd.pos.ctrls[-which(duplicated(sd.pos.ctrls[,1])),]
sd.pos.ctrls <- sd.pos.ctrls[-which(is.na(sd.pos.ctrls[,1])),]Normalizations
TMM Normalization
Normalizing data with TMM, as implemented in edgeR package, and plotting a PCA and an RLE plot of them.
normPropCountsUqua <- NormalizeData(data.to.normalize=filteredCountsProp,
norm.type="tmm",
design.matrix=designMatrix)
PlotPCAPlotlyFunction(counts.data.frame=log1p(normPropCountsUqua),
design.matrix=designMatrix, shapeColname="condition",
colorColname="genotype", xPCA="PC1", yPCA="PC2",
plotly.flag=TRUE, show.plot.flag=TRUE,
prefix.plot="TMM-Norm")## [1] TRUEpal <- RColorBrewer::brewer.pal(9, "Set1")
plotRLE(as.matrix(normPropCountsUqua), outline=FALSE, col=pal[designMatrix$gcondition])
TMM + RUVs Normalization
Applying a RUVs method of RUVSeq package on normalized data, in order to adjust the counts for the unwanted variation. And of corse we plot a PCA and an RLE plot on these data.
library(RUVSeq)
neg.ctrl.list <- rownames(counts.neg.ctrls)
groups <- makeGroups(designMatrix$gcondition)
ruvedSExprData <- RUVs(as.matrix(round(normPropCountsUqua)), cIdx=neg.ctrl.list,
scIdx=groups, k=5)
normExprData <- ruvedSExprData$normalizedCounts
ggp <- PlotPCAPlotlyFunction(counts.data.frame=log1p(normExprData),
design.matrix=designMatrix, shapeColname="condition",
colorColname="genotype", xPCA="PC1", yPCA="PC2",
plotly.flag=FALSE, show.plot.flag=FALSE, save.plot=FALSE,
prefix.plot=NULL)## [1] FALSEggplotly(ggp)dir.create("plots")
save_plot(filename="plots/PCA.pdf", plot=ggp)
pal <- RColorBrewer::brewer.pal(9, "Set1")
plotRLE(normExprData, outline=FALSE, col=pal[designMatrix$gcondition])
edgeR Differential Expression Analysis
Making differential expression analysis with edgeR package on four different contrasts.
Here is a brief legend:
- WTHC5: Wild Type Home Cage Control 5 days
- WTSD5: Wild Type Sleep Deprivation 5 days.
- KOHC5: Knock Out Home Cage Control 5 days.
- KOSD5: Knock Out Sleep Deprivation 5 days.
padj.thr <- 0.05
venn.padgj.thr <- 0.1
desMat <- cbind(designMatrix, ruvedSExprData$W)
colnames(desMat) <- c(colnames(designMatrix), colnames(ruvedSExprData$W))
cc <- c("S3HC5 - WTHC5", "S3SD5 - WTSD5")
rescList1 <- applyEdgeR(counts=filteredCountsProp, design.matrix=desMat,
factors.column="gcondition",
weight.columns=c("W_1", "W_2", "W_3", "W_4", "W_5"),
contrasts=cc, useIntercept=FALSE, p.threshold=1,
is.normalized=FALSE, verbose=TRUE)
names <- names(rescList1)
rescList1 <- lapply(seq_along(rescList1), function(i)
{
attachMeans(normalized.counts=normExprData, design.matrix=desMat,
factor.column="gcondition", contrast.name=names(rescList1)[i],
de.results=rescList1[[i]])
})
names(rescList1) <- namesShank3 Home Cage control VS Wild Type Home Cage Controls
volcano plot
A volcano plot of differential expressed genes.
res.o.map1 <- convertGenesViaMouseDb(gene.list=rownames(rescList1[[1]]),
fromType="ENTREZID")
res.o <- attachGeneColumnToDf(mainDf=rescList1[[1]],
genesMap=res.o.map1,
rowNamesIdentifier="ENTREZID",
mapFromIdentifier="ENTREZID",
mapToIdentifier="SYMBOL")
WriteDataFrameAsTsv(data.frame.to.save=res.o,
file.name.path=paste0(names(rescList1)[1], "_edgeR"))
vp <- luciaVolcanoPlot(res.o, prefix=names(rescList1)[1],
positive.controls.df=NULL,
threshold=padj.thr)
ggplotly(vp)de <- sum(res.o$FDR < padj.thr)
nde <- sum(res.o$FDR >= padj.thr)
detable <- cbind(de,nde)
rownames(detable) <- names(rescList1)[1]
ddetable <- detable
tot.ctrls <- dim(sd.pos.ctrls)[1]
idx.pc <- which(tolower(res.o$gene) %in% tolower(sd.pos.ctrls[,1]))
tot.pc.de <- sum(res.o$FDR[idx.pc] < padj.thr)
tot.pc.nde <- length(idx.pc) - tot.pc.de
wt <- res.o[which(res.o$FDR < padj.thr),]
wt.sign.genes.entrez <- rownames(res.o)[which(res.o$FDR < venn.padgj.thr)]
kowthc5 <- res.o[which(res.o$FDR < padj.thr),]
kowthc5.sign.genes.entrez <- rownames(res.o)[which(res.o$FDR < venn.padgj.thr)]Shank3 Sleed Deprivation VS Wild Type Sleep Deprivation
volcano plot
A volcano plot of differential expressed genes.
rs2.o.map <- convertGenesViaMouseDb(gene.list=rownames(rescList1[[2]]),
fromType="ENTREZID")
res.rs2.o <- attachGeneColumnToDf(mainDf=rescList1[[2]],
genesMap=rs2.o.map,
rowNamesIdentifier="ENTREZID",
mapFromIdentifier="ENTREZID",
mapToIdentifier="SYMBOL")
WriteDataFrameAsTsv(data.frame.to.save=res.rs2.o,
file.name.path=paste0(names(rescList1)[2], "_edgeR"))
vp <- luciaVolcanoPlot(res.rs2.o, positive.controls.df=NULL,
prefix=names(rescList1)[2],
threshold=padj.thr)
ggplotly(vp)de <- sum(res.rs2.o$FDR < padj.thr)
nde <- sum(res.rs2.o$FDR >= padj.thr)
detable <- cbind(de,nde)
rownames(detable) <- names(rescList1)[2]
ddetable <- rbind(ddetable, detable)
pos.df <- cbind(tot.ctrls, tot.pc.de, tot.pc.nde)
colnames(pos.df) <- c("total_p.ctrl", "p.ctrl_de_mapped",
"p.ctrl_notde_mapped")
rownames(pos.df) <- names(rescList1)[2]
kowtsd5 <- res.rs2.o[which(res.rs2.o$FDR < padj.thr),]
kowtsd5.sign.genes.entrez <- rownames(res.rs2.o)[which(res.rs2.o$FDR < venn.padgj.thr)]DE TABLE + Positive Controls table
We present a summarization of the results. The first table is a summarization on how many genes are Differentially Expressed. The second table explains on the first column how many positive controls we have, on the second column how many positive controls have been identified over the differentially expressed genes, and, finally, on the third column how many positive controls have beed identified on the NOT differentially expressed genes.
ddetable## de nde
## S3HC5 - WTHC5 39 14415
## S3SD5 - WTSD5 82 14372pos.df## total_p.ctrl p.ctrl_de_mapped p.ctrl_notde_mapped
## S3SD5 - WTSD5 579 3 553Venn Diagram
KOHC5-WTHC5 vs KOSD5-WTSD5
We take the results of the two contrasts. Knock Out Sleed Deprivation VS Wild Type Sleep Deprivation and Knock Out Home Cage control VS Wild Type Home Cage Controls . And plot the results in a Venn Diagram
source("./R/venn2.R")
gene.map <- convertGenesViaMouseDb(gene.list=rownames(normExprData),
fromType="ENTREZID", toType="SYMBOL")
venn <- Venn2de(x=kowthc5.sign.genes.entrez, y=kowtsd5.sign.genes.entrez,
label1="S3HC5_WTHC5", label2="S3SD5_WTSD5",
title="S3HC5_WTHC5 venn S3SD5_WTSD5", plot.dir="./",
conversion.map=gene.map)
Heatmaps
Setting up the data structures for the heatmps.
source("./R/heatmapFunctions.R")
de.genes.entr <- union(rownames(venn$int), rownames(venn$XnoY))
de.genes.entr <- union(de.genes.entr, rownames(venn$YnoX))
gene.map <- convertGenesViaMouseDb(gene.list=de.genes.entr,
fromType="ENTREZID")
de.genes.symb <- attachGeneColumnToDf(as.data.frame(de.genes.entr,
row.names=de.genes.entr),
genesMap=gene.map,
rowNamesIdentifier="ENTREZID",
mapFromIdentifier="ENTREZID",
mapToIdentifier="SYMBOL")
# de.genes.symb[which(is.na(de.genes.symb$gene)),]
de.genes.symb$gene[which(de.genes.symb$de.genes.entr=="100039826")] <- "Gm2444" ## not annotated in ncbi
de.genes.symb$gene[which(de.genes.symb$de.genes.entr=="210541")] <- "Entrez:210541" ## not annotated in ncbi
de.genes.counts <- normExprData[match(de.genes.symb$de.genes.entr, rownames(normExprData)),]
rownames(de.genes.counts) <- de.genes.symb$gene
de.gene.means <- computeGeneMeansOverGroups(counts=de.genes.counts,
design=designMatrix, groupColumn="gcondition")
library(gplots)
library(clusterExperiment)
color.palette = clusterExperiment::seqPal3#c("black", "yellow")
pal <- colorRampPalette(color.palette)(n = 1000)
library(pheatmap)
filter2 <- rowMeans(de.gene.means)>0
filter <- apply(de.gene.means, 1, function(x) log(x[4]/x[3]) * log(x[2]/x[1]) < 0)
filter[is.na(filter)] <- FALSEHeatmap gene by bene
gene_names <- c("Nr1d1", "Fabp7", "Per3", "Jun", "Elk1", "Fosl2", "Mapk1",
"Mapk3", "Mapk11", "Hmgcr", "Insig1", "Nfil3", "Stat4",
"Kcnv1", "Kcnk1", "Kcnk2", "Dusp10", "Dusp3", "Ptprj",
"Cntn1", "Ntrk2", "Reln", "Sema3a", "Tef", "Hlf", "Nr1d1",
"Prkab2", "Bhlhe41", "Slc6a15", "Slc22a4", "Slc24a4")
idx <- which(!(rownames(de.genes.counts) %in% gene_names))
de.genes.counts1 <- de.genes.counts
rownames(de.genes.counts1)[idx] <- ""
ann.col <- desMat[, c(1:2)]
de.heatmap <- de.genes.counts[filter2,]
set.seed(0)
heatmap_data <- t(scale(t(log(de.heatmap+1)), center = TRUE, scale = FALSE))
ph1 <- pheatmap(heatmap_data, cluster_cols=TRUE, scale="none",
color=pal, border_color=NA, fontsize_row=10, kmeans_k=3, annotation_col=ann.col)
clusterized.genes <- as.data.frame(ph1$kmeans$cluster)
gene.map <- convertGenesViaMouseDb(gene.list=rownames(clusterized.genes), fromType="SYMBOL")
converted.clusterized.gens <- attachGeneColumnToDf(mainDf=clusterized.genes, genesMap=gene.map,
rowNamesIdentifier="SYMBOL", mapFromIdentifier="SYMBOL", mapToIdentifier="ENTREZID")
converted.clusterized.gens$gene[which(rownames(converted.clusterized.gens)=="Gm2444")] <- "100039826" ## not annotated in ncbi
converted.clusterized.gens$gene[which(rownames(converted.clusterized.gens)=="Entrez:210541")] <- "210541" ## not annotated in ncbi
converted.clusterized.gens <- converted.clusterized.gens[order(converted.clusterized.gens$`ph1$kmeans$cluster`),]
save_pheatmap_pdf(filename="plots/heatmap_kmeans_k3.pdf", plot=ph1, width=20, height=20)## png
## 2WriteDataFrameAsTsv(data.frame.to.save=converted.clusterized.gens, file.name.path="plots/clustered_genes_by_kmeans3")
ord.de.genes.counts <- de.heatmap[match(rownames(converted.clusterized.gens), rownames(de.heatmap)),]
idx <- which(!(rownames(ord.de.genes.counts) %in% gene_names))
rownames(ord.de.genes.counts)[idx] <- ""
gaps.row <- c()
for(i in c(1:3))
{
li <- length(which(converted.clusterized.gens$`ph1$kmeans$cluster`==i))
l <- ifelse(i!=1, gaps.row[i-1]+li, li)
gaps.row <- c(gaps.row, l)
}
heatmap_data_scaled <- t(scale(t(log(ord.de.genes.counts+1)), center = TRUE, scale = TRUE))
library(dendextend)
column_dend <- as.dendrogram(hclust(dist(t(heatmap_data_scaled))))
ord <- labels(column_dend)
ord[11:15] <- labels(column_dend)[16:20]
ord[16:20] <- labels(column_dend)[11:15]
column_dend <- rotate(column_dend, ord)
ph1 <- pheatmap(heatmap_data_scaled, cluster_cols=as.hclust(column_dend), scale="none",
color=pal, border_color=NA, fontsize_row=9, fontsize_col=9, cluster_rows=FALSE,
annotation_col=ann.col, gaps_row=gaps.row)
save_pheatmap_pdf(filename="plots/heatmap_gg_k3.pdf", plot=ph1, width=20, height=20)## png
## 2other heatmaps
heatmap_data <- t(scale(t(log(ord.de.genes.counts+1)), center = TRUE, scale = FALSE))
ph1 <- pheatmap(heatmap_data, cluster_cols=as.hclust(column_dend), scale="none",
color=pal, border_color=NA, fontsize_row=9, fontsize_col=9, cluster_rows=FALSE,
annotation_col=ann.col, gaps_row=gaps.row,
breaks = c(min(heatmap_data), seq(quantile(as.vector(heatmap_data), .01), quantile(as.vector(heatmap_data), .99), length.out = length(pal)-1), max(heatmap_data)))
save_pheatmap_pdf(filename="plots/heatmap_gg_k3_no_scale.pdf", plot=ph1, width=20, height=20)## png
## 2## Only SD samples
heatmap_data <- t(scale(t(log(de.heatmap[, grep("^SD", desMat[,2])]+1)), center = TRUE, scale = FALSE))
ph1 <- pheatmap(heatmap_data, cluster_cols=TRUE, scale="none",
color=pal, border_color=NA, fontsize_row=10, kmeans_k=2, annotation_col=ann.col)
clusterized.genes <- as.data.frame(ph1$kmeans$cluster)
gene.map <- convertGenesViaMouseDb(gene.list=rownames(clusterized.genes), fromType="SYMBOL")
converted.clusterized.gens <- attachGeneColumnToDf(mainDf=clusterized.genes, genesMap=gene.map,
rowNamesIdentifier="SYMBOL", mapFromIdentifier="SYMBOL", mapToIdentifier="ENTREZID")
converted.clusterized.gens$gene[which(rownames(converted.clusterized.gens)=="Gm2444")] <- "100039826" ## not annotated in ncbi
converted.clusterized.gens$gene[which(rownames(converted.clusterized.gens)=="Entrez:210541")] <- "210541" ## not annotated in ncbi
converted.clusterized.gens <- converted.clusterized.gens[order(converted.clusterized.gens$`ph1$kmeans$cluster`),]
ord.de.genes.counts <- de.heatmap[match(rownames(converted.clusterized.gens), rownames(de.heatmap)),]
idx <- which(!(rownames(ord.de.genes.counts) %in% gene_names))
rownames(ord.de.genes.counts)[idx] <- ""
gaps.row <- c()
for(i in c(1:2))
{
li <- length(which(converted.clusterized.gens$`ph1$kmeans$cluster`==i))
l <- ifelse(i!=1, gaps.row[i-1]+li, li)
gaps.row <- c(gaps.row, l)
}
heatmap_data <- t(scale(t(log(ord.de.genes.counts[, grep("^SD", desMat[,2])]+1)), center = TRUE, scale = TRUE))
ph1 <- pheatmap(heatmap_data, cluster_cols=TRUE, scale="none",
color=pal, border_color=NA, fontsize_row=9, fontsize_col=9, cluster_rows=FALSE,
annotation_col=ann.col, gaps_row=gaps.row)
save_pheatmap_pdf(filename="plots/heatmap_gg_sd_only.pdf", plot=ph1, width=20, height=20)## png
## 2Group gene profiles
Group gene profiles by genotype
g <- geneGroupProfileRows(normalized.counts=normExprData, design.matrix=designMatrix,
gene.names=c("Nr1d1", "Hlf", "Per3", "Bhlhe41", "Tef"),
res.o=de.genes.symb, show.plot=TRUE, plotly.flag=FALSE, log.flag=TRUE)
ggplotly(g)save_plot(filename=paste0("plots/", "Nr1d1_Hlf_Per3_Bhlhe41_Tef", "_log_gene_profile_genotype.pdf"), plot=g,
base_height=15, base_width=15)Group gene profiles by condition
g <- geneGroupProfileRowsRev(normalized.counts=normExprData, design.matrix=designMatrix,
gene.names=c("Nr1d1", "Hlf", "Per3", "Bhlhe41", "Tef"),
res.o=de.genes.symb, show.plot=TRUE, plotly.flag=FALSE, log.flag=TRUE)
ggplotly(g)save_plot(filename=paste0("plots/", "Nr1d1_Hlf_Per3_Bhlhe41_Tef", "_log_gene_profile_condition.pdf"), plot=g,
base_height=15, base_width=15)Circadian Analysis
Analysis for activity
wt <- read_xlsx("data/Activity_analysis_4_R.xlsx", sheet = 1)
mut <- read_xlsx("data/Activity_analysis_4_R.xlsx", sheet = 2)
wt <- wt %>%
bind_cols(WT.M=rep("WT", nrow(wt)), time = decimal_date(ymd(wt$`Total_revolutions/day`)), .) %>%
gather(mice, activity, -c(1:5)) %>%
mutate(time = time-min(time)) %>%
dplyr::select(-`Total_revolutions/day`)
mut <- mut %>%
bind_cols(WT.M=rep("M", nrow(mut)), time = decimal_date(ymd(mut$`Total_revolutions/day`)), .) %>%
gather(mice, activity, -c(1:5)) %>%
mutate(time = time-min(time)) %>%
dplyr::select(-`Total_revolutions/day`)
data <- wt %>% bind_rows(mut)
data <- data %>% filter(week>=3)
data$mice <- factor(data$mice, levels= unique(data$mice))
data$time_scaled <- scale(data$time, scale=FALSE)
data$period <- factor(data$period, levels= unique(data$period))
data$WT.M <-factor(data$WT.M, levels=c("WT", "M"))
mod <- lme(activity ~ time_scaled * WT.M, random=~1|mice, data = data)
cat("Estimates, errors and the significance")## Estimates, errors and the significancesummary(mod)## Linear mixed-effects model fit by REML
## Data: data
## AIC BIC logLik
## 8681.339 8705.303 -4334.67
##
## Random effects:
## Formula: ~1 | mice
## (Intercept) Residual
## StdDev: 14936.81 11161.46
##
## Fixed effects: activity ~ time_scaled * WT.M
## Value Std.Error DF t-value p-value
## (Intercept) 38778.45 5335.29 388 7.268296 0.0000
## time_scaled -20486.52 35588.68 388 -0.575647 0.5652
## WT.MM -20324.26 7810.06 13 -2.602317 0.0219
## time_scaled:WT.MM -289880.69 52096.49 388 -5.564304 0.0000
## Correlation:
## (Intr) tm_scl WT.MM
## time_scaled 0.000
## WT.MM -0.683 0.000
## time_scaled:WT.MM 0.000 -0.683 0.000
##
## Standardized Within-Group Residuals:
## Min Q1 Med Q3 Max
## -2.69133816 -0.63470177 0.03277689 0.63109234 3.19701990
##
## Number of Observations: 405
## Number of Groups: 15cat("Bootstrap confidence intervals for the estimates")## Bootstrap confidence intervals for the estimatesmod_lmer <- lmer(activity ~ time_scaled * WT.M + (1|mice), data = data)
confint.merMod(mod_lmer, method = "boot", nsim = 999)## 2.5 % 97.5 %
## .sig01 8955.95 20503.825
## .sigma 10389.64 12032.640
## (Intercept) 28479.16 48421.280
## time_scaled -87582.69 48954.982
## WT.MM -36065.65 -4532.543
## time_scaled:WT.MM -384418.16 -187596.734cat("ANOVA table")## ANOVA tableanova.lme(mod, type = "marginal", adjustSigma = F)## numDF denDF F-value p-value
## (Intercept) 1 388 52.82812 <.0001
## time_scaled 1 388 0.33137 0.5652
## WT.M 1 13 6.77206 0.0219
## time_scaled:WT.M 1 388 30.96148 <.0001Analysis for alpha
wt <- read_xlsx("data/Alpha_Activity_analysis_4_R.xlsx", sheet = 1, na = "NA")
mut <- read_xlsx("data/Alpha_Activity_analysis_4_R.xlsx", sheet = 2, na = "NA")
wt <- wt %>%
bind_cols(WT.M = rep("WT", nrow(wt)), time = decimal_date(ymd(wt$`Total_revolutions/day`)), .)%>%
gather(mice, alpha, -c(1:5)) %>%
mutate(time = time-min(time)) %>%
dplyr::select(-`Total_revolutions/day`)
mut <- mut %>%
bind_cols(WT.M=rep("M", nrow(mut)), time = decimal_date(ymd(mut$`Total_revolutions/day`)), .)%>%
gather(mice, alpha, -c(1:5)) %>%
mutate(time = time-min(time)) %>%
dplyr::select(-`Total_revolutions/day`)
alpha_data <- wt %>% bind_rows(mut)
alpha_data <- alpha_data %>% filter(week>=3)
alpha_data<- na.omit(alpha_data)
alpha_data$mice <- factor(alpha_data$mice, levels= unique(alpha_data$mice))
alpha_data$time_scaled <- scale(alpha_data$time, scale=FALSE)
alpha_data$period <- factor(alpha_data$period, levels= unique(alpha_data$period))
alpha_data$WT.M <- factor(alpha_data$WT.M, levels=c("WT", "M"))
alpha_data$alpha <- as.numeric(alpha_data$alpha)
mod1 <- lme(alpha ~ time_scaled * WT.M, random=~1|mice, data = alpha_data, na.action = na.omit)
cat("Estimates, errors and the significance")## Estimates, errors and the significancesummary(mod1)## Linear mixed-effects model fit by REML
## Data: alpha_data
## AIC BIC logLik
## 2068.243 2091.978 -1028.121
##
## Random effects:
## Formula: ~1 | mice
## (Intercept) Residual
## StdDev: 0.6720236 3.405597
##
## Fixed effects: alpha ~ time_scaled * WT.M
## Value Std.Error DF t-value p-value
## (Intercept) 10.101010 0.334981 373 30.154013 0.0000
## time_scaled -16.267322 11.031558 373 -1.474617 0.1412
## WT.MM -0.714526 0.490361 13 -1.457142 0.1688
## time_scaled:WT.MM 2.360361 16.148547 373 0.146166 0.8839
## Correlation:
## (Intr) tm_scl WT.MM
## time_scaled 0.000
## WT.MM -0.683 0.000
## time_scaled:WT.MM 0.000 -0.683 0.000
##
## Standardized Within-Group Residuals:
## Min Q1 Med Q3 Max
## -2.73631345 -0.48276146 0.06646042 0.49709145 3.94949030
##
## Number of Observations: 390
## Number of Groups: 15cat("Bootstrap confidence intervals for the estimates")## Bootstrap confidence intervals for the estimatesmod1_lmer <- lmer(alpha ~ time_scaled * WT.M + (1|mice), data = alpha_data)
confint.merMod(mod1_lmer, method = "boot", nsim = 999)## 2.5 % 97.5 %
## .sig01 0.000000 1.126553
## .sigma 3.147126 3.655970
## (Intercept) 9.461287 10.763733
## time_scaled -38.593893 4.970986
## WT.MM -1.680165 0.235239
## time_scaled:WT.MM -29.410802 34.100068cat("ANOVA table")## ANOVA tableanova.lme(mod1, type = "marginal", adjustSigma = F)## numDF denDF F-value p-value
## (Intercept) 1 373 909.2645 <.0001
## time_scaled 1 373 2.1745 0.1412
## WT.M 1 13 2.1233 0.1688
## time_scaled:WT.M 1 373 0.0214 0.8839Analysis for period
wt <- read_xlsx("data/Period_analysis_4_R.xlsx", sheet = 1) %>% gather(mice, value, -1)
wt <- data.frame(WT.M=rep("WT", nrow(wt))) %>% bind_cols(wt)
mut <- read_xlsx("data/Period_analysis_4_R.xlsx", sheet = 2) %>% gather(mice, value, -1)
mut <- data.frame(WT.M=rep("M", nrow(mut))) %>% bind_cols(mut)
period_data <- wt %>% bind_rows(mut)
period_data$value <- as.numeric(period_data$value)
mod2 <- lme(value~ week * WT.M, random = ~1|mice, data = period_data)
cat("Estimates, errors and the significance")## Estimates, errors and the significancesummary(mod2)## Linear mixed-effects model fit by REML
## Data: period_data
## AIC BIC logLik
## 309.1875 328.7 -144.5938
##
## Random effects:
## Formula: ~1 | mice
## (Intercept) Residual
## StdDev: 0.7251103 3.268825
##
## Fixed effects: value ~ week * WT.M
## Value Std.Error DF t-value p-value
## (Intercept) 23.721429 1.265532 39 18.744234 0.0000
## weekDD_Week_2 -3.381429 1.747260 39 -1.935275 0.0602
## weekDD_Week_3 2.055714 1.747260 39 1.176536 0.2465
## weekLD_Week_3 0.208571 1.747260 39 0.119371 0.9056
## WT.MWT 0.137321 1.732901 13 0.079244 0.9380
## weekDD_Week_2:WT.MWT 3.251429 2.392535 39 1.358989 0.1820
## weekDD_Week_3:WT.MWT -2.426964 2.392535 39 -1.014390 0.3166
## weekLD_Week_3:WT.MWT -0.063571 2.392535 39 -0.026571 0.9789
## Correlation:
## (Intr) wkDD_W_2 wkDD_W_3 wkLD_W_3 WT.MWT wDD_W_2:
## weekDD_Week_2 -0.690
## weekDD_Week_3 -0.690 0.500
## weekLD_Week_3 -0.690 0.500 0.500
## WT.MWT -0.730 0.504 0.504 0.504
## weekDD_Week_2:WT.MWT 0.504 -0.730 -0.365 -0.365 -0.690
## weekDD_Week_3:WT.MWT 0.504 -0.365 -0.730 -0.365 -0.690 0.500
## weekLD_Week_3:WT.MWT 0.504 -0.365 -0.365 -0.730 -0.690 0.500
## wDD_W_3:
## weekDD_Week_2
## weekDD_Week_3
## weekLD_Week_3
## WT.MWT
## weekDD_Week_2:WT.MWT
## weekDD_Week_3:WT.MWT
## weekLD_Week_3:WT.MWT 0.500
##
## Standardized Within-Group Residuals:
## Min Q1 Med Q3 Max
## -5.938352181 -0.067963537 -0.001414478 0.093674882 1.778532447
##
## Number of Observations: 60
## Number of Groups: 15cat("Bootstrap confidence intervals for the estimates")## Bootstrap confidence intervals for the estimatesmod2_lmer <- lmer(value ~ week * WT.M + (1|mice), data = period_data)
confint.merMod(mod2_lmer, method = "boot", nsim = 999)## 2.5 % 97.5 %
## .sig01 0.000000 2.14316485
## .sigma 2.513938 3.84935166
## (Intercept) 21.295014 26.08264006
## weekDD_Week_2 -6.777346 0.06162009
## weekDD_Week_3 -1.202367 5.34826111
## weekLD_Week_3 -3.176616 3.68604026
## WT.MWT -3.217573 3.26535504
## weekDD_Week_2:WT.MWT -1.222345 8.16487237
## weekDD_Week_3:WT.MWT -6.663714 1.92943012
## weekLD_Week_3:WT.MWT -4.658113 4.21914440cat("ANOVA table")## ANOVA tableanova.lme(mod2, type = "marginal", adjustSigma = F)## numDF denDF F-value p-value
## (Intercept) 1 39 351.3463 <.0001
## week 3 39 3.3611 0.0282
## WT.M 1 13 0.0063 0.9380
## week:WT.M 3 39 1.9008 0.1454